The modified soil-adjusted vegetation index (MSAVI) and its later revision, MSAVI2, are soil adjusted vegetation indices that seek to address some of the limitation of NDVI when applied to areas with a high degree of exposed soil surface. The problem with the original soil-adjusted vegetation index (SAVI) is that it required specifying the soil-brightness correction factor (L) through trial-and-error based on the amount of vegetation in the study area. Not only did this lead to the majority of people just using the default L value of 0.5, but it also created a circular logic problem of needing to know what the vegetation amount/cover was before you could apply SAVI which was supposed to give you information on how much vegetation there was. Qi et al. (1994a) developed the MSAVI, and later the MSAVI2 (Qi et al. 1994b) to more reliably and simply calculate a soil brightness correction factor.
The formula for calculating MSAVI itself is the same as the formula for calculating SAVI:
where RED is the red band reflectance from a sensor, NIR is the near infrared band reflectance, and L is the soil brightness correction factor. The difference between SAVI and MSAVI, however, comes in how L is calculated. In SAVI, L is estimated based on how much vegetation there is (but it’s generally left alone at a compromise of 0.5). MSAVI uses the following formula to calculate L:
“Feature Space” images like this one are created by graphing the red band reflectance value against the near infrared band values for every pixel in an image. The colors in the image represent how many pixels have that RED:NIR value combination – warmer colors mean more, cooler colors mean fewer pixels. When a red vs. near infrared feature space plot is created, a soil-line can be identified by the combinations of red and near-infrared pixel values where vegetation no longer occurs. The slope of this soil line is used in calculating L in the MSAVI equation.
Qi et al. (1994b), starting with the MSAVI equation, substituted 1-MSAVI(n) for a range of n and then solved the equation recursively until MSAVI(n)=MSAVI(n-1). This yields the following formula, commonly called MSAVI2, which eliminates the need to find the soil line from a feature-space plot or even explicitly specify the soil brightness correction factor:
The output of MSAVI or MSAVI2 is a new image layer representing vegetation greenness with values ranging from -1 to +1.
Successful Rangeland Uses
MSAVI has been used in a number of rangeland studies where it has often been correlated to field data on vegetation cover (Senseman et al. 1996a, Senseman et al. 1996b, Chen 1999), biomass and/or leaf area index (Smith et al. 2005, Phillips et al. 2009), and as an input layer for mapping land cover or vegetation classes. Liu and Wang (2005) used MSAVI as one indicator to monitor desertification in China. Phillips et al. (2009) used biomass estimates derived from MSAVI and field data to estimate grazing capacity in northern U.S. prairies.
- Phillips, R., Beeri, O., Scholljegerdes, E., Bjergaard, and J. Hendrickson. 2009. Integration of geospatial and cattle nutrition information to estimate paddock grazing capacity in Northern US prairie. Agricultural Systems 100:72-79.
- Liu, A. and J. Wang. 2005. Monitoring desertification in arid and semi-arid areas of China with NOAA-AVHRR and MODIS data. Geoscience and Remote Sensing Symposium. IEEE, IGARSS ’05.
- Smith, A., Freemantle, J., Nadeau, C., Wehn, H., Zwick, H., and J. Miller. 2005. Leaf area index map generation using CHRIS data. Presentation given at the 3rd CHRIS Proba Workshop. Accessed January 7, 2010. http://earth.esa.int/workshops/chris_proba_05/presentations/Session_2/04C.Nadeau_Session2.pdf
- Chen, Y. 1999. Correlation of saltbush cover measurements to TM wavebands and vegetation indices. Geoscience and Remote Sensing Symposium, IEEE IGARSS ’99.
- Senseman, G.M., Tweddale, S.A., Anderson, A.B., and C.F. Bagley. 1996. Correlation of land condition trend analysis (LCTA) rangeland cover measures to satellite-imagery-derived vegetation indices. US Army Corps of Enginers USACERL Technical Report 97/07.
- Senseman, G.M., Bagley, C.F., and S.A. Tweddale. 1996. Correlation of rangeland cover measures to satellite-imagery-derived vegetation indices. Geocarto International 11(3):29-38.
- Jiang, Z., Huete, A.R., Li, J., and J. Qi. 2007. Interpretation of the modified soil-adjusted vegetation index isolines in red-NIR reflectance space. Journal of Applied Remote Sensing doi:10.1117/1.2709702
- Qi J., Chehbouni A., Huete A.R., Kerr Y.H., 1994. Modified Soil Adjusted Vegetation Index (MSAVI). Remote Sens Environ 48:119-126.
- Qi J., Kerr Y., Chehbouni A., 1994. External factor consideration in vegetation index development. Proc. of Physical Measurements and Signatures in Remote Sensing, ISPRS, 723-730.
- Ray, T.W. (Accessed Jan 5, 2010). A FAQ on vegetation in remote sensing. http://www.yale.edu/ceo/Documentation/rsvegfaq.html.
One significant limitation of the MSAVI is that it sacrifices some overall sensitivity to changes in vegetation amount/cover to correct for the soil surface brightness. Hence, MSAVI may not be as sensitive to vegetation change as another index like NDVI. MSAVI would also be more sensitive to differences in atmospheric conditions between areas or times.
MSAVI requires only a red and a near infrared band to calculate.
MSAVI is relatively easy to calculate and can be done with any remote sensing package (e.g., ERDAS Imaging, ENVI, Idrisi) or with a GIS program that can handle raster processing (e.g., ArcGIS with Spatial Analyst extension, GRASS).
- T. Ray’s Vegetation Remote Sensing FAQ – http://www.yale.edu/ceo/Documentation/rsvegfaq.html
- ORFEO Toolbox Wiki entry on MSAVI – http://wiki.orfeo-toolbox.org/index.php/MSAVI
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